Optimization Using Neural Network Modeling and Swarm Intelligence in the Machining of Titanium (Ti 6Al 4V) Alloy

The process of titanium´s machining in the aerospace industry today is by trial and error, it produce non efficient results, because this material is classified by the high chemical reaction with other materials and its low thermal conductivity such as a difficult to machine, so the process of finding the correct parameters for machining are hard to determine, and today researchers are looking to develop new models to predict and optimize these parameters. A developed optimization algorithm called particle swarm optimization is used to find optimum process parameters. Accordingly, the results indicate that a system where neural network is used to model and predict process outputs and particle swarm optimization is used to obtain optimum process parameters can be successfully applied to multi-objective optimization of titanium´s machining process.